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Alternatives to cogmem

  • A
    license
    B
    quality
    D
    maintenance
    Local Markdown-backed memory tools for Codex and other MCP-capable agents. Exposes durable agent knowledge via CLI and MCP server.
    5
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep
    5
    1
    MIT

Related Servers

  • A
    license
    A
    quality
    A
    maintenance
    Local-first memory layer for AI coding agents — captures issues, attempts, fixes, and decisions, and warns at git commit before you repeat a mistake.
    15
    794
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Local-first project memory for AI coding agents. Records failed attempts, fragile files, and decisions per repo, and warns the agent via hooks before it repeats a recorded mistake.
    6
    50
    MIT
  • F
    license
    Not graded
    quality
    A
    maintenance
    Local-first memory for AI agents with evidence-backed recall, deterministic trust verdicts, self-inspection, and a tamper-evident audit history.
    3
    -
  • A
    license
    Not graded
    quality
    A
    maintenance
    Provides a local-first memory layer for coding agents, storing principles, rules, and corrections on-device and surfacing them at the moments they apply so agents act on the intended outcomes without sending data to the cloud.
    20,000
    1
    AGPL 3.0
  • A
    license
    A
    quality
    A
    maintenance
    Provides shared long-term memory and proactive suggestions across AI coding agents, enabling persistent user preferences and behavior rules that are remembered and applied across different tools. Includes memory capture, recall, extraction, and suggestion tools with a local-first and privacy-focused design.
    20
    1
    MIT

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